Real-Time Biosurveillance Program Pilot - India & Sri Lanka
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Transcript of Real-Time Biosurveillance Program Pilot - India & Sri Lanka
Presentation Title
Nuwan WaidyanathaLIRNEasia
Email: [email protected]
http://www.lirneasia.net/profiles/nuwan-waidyanathaMobile: +8613888446352 (cn) +94773710394 (lk)
Conference Theme
Month day, 2010Location
This work was carried out with the aid of a grant from the International Development Research Centre, Canada.
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Early detection and mitigation of common diseases and pandemics
Real-Time Biosurveillance Program to Revolutionize disease surveillance and notification
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INTRODUCTION TO THE RESEARCH Synergies of RTBP and Early Warning Systems Research question and specific objectives
DISEASE INFORMATION REQUIREMENTS Determinants of notifiable diseases in India and Sri Lanka Cycle of data collection, analysis, and dissemination
COMMUNICATION SYSTEM EVALUATIONS Data collection :: mHealthSurvey mobile application Event detection :: T-Cube Web Interface Disseminations :: Sahana Messaging/Alerting Module Cost effectiveness, efficiencies, and sensitivity
CONCLUSIONS & REFERENCES
Outline
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Synopsis of IDRC funded PANACeA projects
PAN Asian Collaboration for Evidence-based e-Health Adoption and Application
Initiative to generate evidence in the field of e-health within the Asian context, by forming a network of researchers and research projects from developing Asian countries.
http://www.aku.edu/CHS/panacea/about.shtml
Some PANACeA Initiatives (more :: http://tinyurl.com/39ypljm )
❏ Outbreak Management System
❏ Systematic review of ICTs in disasters
❏ e-Health system for community health care recording and reporting
❏ Mobile telemedicine system for ambulance and movable community health care
❏ Mobile telemedicine kit for disaster relief
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Panacea THIRRA (http://thirra.primacare.org.my): Portable system from telehealth and health information in rural & remote areas
Cell-Life: preventing HIV/Aids (): monitoring and intervention programs by communicating data via mobile phone GPRS connected workstations
EpiSurveyor Software developed by DataDyne (): Desktop tool to develop “forms” for handheld mobile devices; J2ME mobile phone solution, Tested on PDAs in Uganda, Tanzania; on “CDC Maternal Health Evaluation Forms”;live stock development board Sri Lanka
OpenRosa/JavaRosa (): consortium developing standards based tools for collecting data via mobile phones, analyzing data, and reporting data via mobile phones, Free software downloads – “Gather” is a project supporting the openrosa work and testing solutions in Africa
D-Tree Offers Childcare solutions ():They use javarosa code base; e-MICI project in Uganda – household questionnaire to monitor Child diseases; doing work with BRAC in Tanzania on gathering household health info
ComCare: XForms based solution for collecting community health information
InSTEDD: create and advocate open source tools (): Social network approach, Tested in Mekong river basin, Event base surveillance techniques
Click Diagnostics (): collect patient household data, stakeholders to run custom analyze , program to target disease areas and improve intervention (e.g. HIV/AIDS staging and regimen management, cervical cancer screening, malaria surveillance, TB surveillance)
Other m-Health Initiatives on disease control
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Disease Surveillance
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Disease Surveillance
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Sensor
Detection
Decision
Physical World
Broker
Response
Doctrine of Real-Time Biosurveillance (RTBP)
Health Providers,Relief Workers
Observe Relevant Data
RTBPm-HealthSurvey
Record and Transmit Data GSM phone
network
RTBPServer and Database
Store Data
RTBPInteractive
Visualization, Analysis and
Event Detection Software
Monitor Data
AffectedPopulation
Automated Alerts
Interactive Analytics
Internet, GSM
network
Internet
Analysts, Health Officials,Epidemiologists, Decision Makers
Manage Relief Effort
HealthDisaster
Management
RTBP?
RTBP?
RTBP?
RTBP?
RTBP?
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Research Question: “Can software programs that analyze health statistics and mobile phone applications that send and receive the health information potentially be effective in the early detection and mitigation of disease outbreaks?”
Evaluating a Real-Time Biosurveillance Program: Pilot
Specific Objectives
Evaluating the effectiveness of the m-Health RTBP for detecting and reporting outbreaks
Evaluating the benefits and efficiencies of communicating disease information
Contribution of community organization and gender participation
Develop a Toolkit for assessing m-Health RTBPs
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Research Question: “Can software programs that analyze health statistics and mobile phone applications that send and receive the health information potentially be effective in the early detection and mitigation of disease outbreaks?”
mHealthSurvey a data entry software works on any standard java mobile phone. A typical record contains the patient visitation date, location, gender, age, disease, symptoms, and signs. Data is transmitted over GPRS cellular networks.
T-Cube Web Interface (TCWI) is an Internet browser based tool to visualize and manipulate large spatio-temporal data sets. Epidemiologists can pin down a potential outbreak of, for instance, a gastrointestinal disease among children in the Sevanipatti PHC health division.
Sahana Alerting Broker (SABRO) allows for the generic dissemination of localized and standardized interoperable messages. Selected groups of recipients would receive the single-entry of the message via SMS, Email, and Web.
Data Collection Event Detection Alerting
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Black arrows: current manual paper/postal system for health data collection and reporting
Red lines: RTBP mobile phone communication system for heath data collection and reporting
Problem the RTBP is trying to solve in Sri Lanka
Reduce 07-15 day delays to Minutes
Re-engineer the limited disease activated passive surveillance to Comprehensive Active surveillance
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Black arrows: current manual paper/postal system for health data collection and reporting
Red lines: RTBP mobile phone communication system for heath data collection and reporting
Problem RTBP is trying to solve in India
Reduce 07-15 day delays to Minutes
Re-engineer the limited disease activated passive surveillance to Comprehensive Active surveillance
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RTBP high level system diagram
Skip the paper
Actors, processes, and information flow of the proposed data collection, event detection, and situational-awareness/alerting real-time program
1. Health records digitized by health workers in Thirupathur block using mobile phones.
2. Disease, symptoms, and demographic information transmitted across GSM mobile network to central database.
3. Data analysed by trained staff at the IDSP and PHC Departments.
4. Automated event detection algorithms process a daily ranked set of possible disease outbreaks, which are presented to IDSP and PHC staff.
5. List of possible outbreaks examined by IDSP and PHC staff to determine likelihood of an adverse event.
6. Confirmed adverse events disseminated to Medical Officers, HIs, nurses, and other health officials, within affected geographic area.
7. Condensed version of the alert pushed through SMS to get immediate attention of the recipients.
8. More descriptive message emailed and published on the web (also accessible through mobile phone).
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Why use mobile for data collection and dissemination?
These charts with demand side statistics were taken from LIRNEasia's teleuse at BOP 3 study - http://lirneasia.net/projects/2008-2010/bop-teleuse-3/
0%
20%
40%
60%
80%
100%
Bangladesh Pakistan India Sri Lanka Philippines Thailand
Phone ownership (% of BOP teleusers)
Fixed only
Mobile + fixed
Mobile only
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Evaluation metric verticals and horizontals
Three verticals – data collection, event detection and reporting
Four layers – Institutional, content, application, Transport
Arrows showing the Interoperability between layers and verticals
Objectively assess by calculating various indicators: costs, efficiencies, error rates, etc
Subjectively assess through interviews and simulations
Talk aboutthis
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The pilot in India and Sri Lanka
24 Health Sub Center Village Nurses
4 Public Health Center Sector Health Nurses, Health Inspectors, and Data Entry Operators
1 Integrated Disease Surveillance Program Unit of the Deputy Director of Health Services
Thirupathur Block, Sivagangai District, Tamil Nadu, India
12 District/Base Hospitals and Clinics
15 Sarvodaya Suwadana Center Assistants
4 Medical Officer of Health divisions & 1 Regional Epidemiology Unit
Kurunegala District, Wayamba Province, Sri Lanka
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Why digitize the frontline health data?
❒ Activated passive surveillance
❒ Data limited to 25 infectious diseases (avg LK=70 TN=600 monthly health records per district)
❒ Only 20% of the diagnosis are confirmed, rest are probable and suspected, likely to miss signs of emerging outbreak
❒ No data on other-communicable or chronic diseases
❒ Trend analysis is based on < 0.05% of patient population
❒ Planing and resource allocation is based on expert opinion, departments are not data driven
❒ Active surveillance with situational awareness
❒ All data on diseases infectious and chronic diseases (avg 50000 monthly health records per district)
❒ Collect symptoms and signs for syndromic surveillance;
❒ A richer dataset with more attributes
❒ Statistics are on actual patient visitations; even data on snake bites and accidents
❒ Comprehensive data can be used for long term planning and move towards data driven departments
Existing RTBP
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Data collection process
(1) Patient is received by the Nurse
(2) Nurse issues a diagnosis chit to patient fills in Name, Age, Gender, and OPD No
(3) Medical Officer fills in the chit with diagnosis and treatment
(4) Patient presents chit to pharmacy to receive medication
(5) Data Entry Operator digitizes and submits the data
1
2
3
4
5
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mHealthSurvey Midlet by IIT-M
(a) Main menu
(b) Profile registration
(c) Retrieve locations
(d) Patient record screen I
(e) Patient record screen II
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mHealthSurvy software design
J2ME: Built on Java 2 Micro edition
CDC: works with CDC 1.1 and above (JSR)
MIDP: works with MIDP 2.0 or above
GPRS: transport technology
Each record is 2kb and costs INR 0.01 or LKR 0.02 (USD 0.0002) i.e < USD 10 Handset/Month
Mobile phone around US$ 100
Tested on Nokia3110c, Motorola SLVR L7, Gionee v6900. Amoi A636, Sony Ericsson s302c
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mHealthSurvey Certification Exercise
Sri Lanka India Benchmark0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Part IIIPart IIPart I
Country
Scor
es (
Min
=0;
Max
=10
0 )
Exercise Benchmark Sri Lanka IndiaPart I – installation and configuration (min) 12.00 10.75 17.48Part II – submit up to 6 records (min) 20.00 10.80 27.26Par III – standard operating procedures (points) 50.00 20.43 15.00
Outcome categorizationCertified trainers ( > 90 points) 02 of 15 NilCertified Users (90 ≥ points > 70) 13 of 15 04 of 23Uncertified (points ≤ 70) --- 19 of 23
Younger (age 18 – 35) Sri Lankan data entry assistants with no health training and no prior experience were quick to adapt to the technology
The relatively older (age 35 – 55) trained health workers in India with 10 – 25 yr experience had difficulty adapting to the software but have improved after some intervention and additional training
Average Country Scores
Exercise conducted 2 months after training and use of the mobile health software
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Quality of the digitized data
The 23% noisy data in India subsided to less than 4% after informing the consequences of false detections (SNR for sub intervals: 0.18, 0.40. 0.31. 0.04, 0.07)
Data quality = Signal to Noise Ratio (SNR); i.e. number records with errors/records submitted
Assistants in Sri Lanka with no formal health training and no affiliation to the hospitals/clinics had no incentive to correct the 45% errors (SNR for sub intervals: 0.58, 0.30, 0.53, 0.57, 0.17)
1 Low quantities of data received from Health Sub Centers2 Volume of records were better after including Primary Health Centers3 Holiday effect: no records received4 Learning curve getting medical officers to adopt to the new procedures of writing the diagnosis5 Release of mHealthSurvey v1.3 with better predictive text
INDIA
SRI LANKA
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Timeliness of data submission
Finding time to complete the records without disrupting current work flow was a significant barrier for real- time data submission (sub interval delay rates 0.28, 0.09, 0.21, 0.38, 0.44, 0.48, 0.68)
Timeliness = submitting the patient’s record the same day as the patient visitation
Data entry assistants have no other role besides digitizing records but see delays proportional to the patient visitation counts (sub interval delay rates: 0.10, 0.27, 0.25, 0.36, 0.53, 0.21).
1 Users with dysfunctional phones where sharing and were sending data on the weekends or when friends phone was available for borrowing
INDIA
SRI LANKA
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Digitizing problems that affect the categorical data
SNOMED-CT
LOINC
UMLS
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Fix the data collection shortcomings: noisy and off-time
0102
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Noisy Clear
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Off-Time Real-Time
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Need solutions
here
From: 01-Sep-2009 To: 30-May-2010
Case Records:
220000+
From: 01-Jun-2009 To: 30-May-2010
Case Records:
81000+
Noisy vs Cleandata
Real-Time vs Off-Timedata
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Observations of the data digitizing uncertainties
No observations for India in this quadrant → Data submitted by health workers in India is consistent
Diseases with higher counts but occurring only in a single location; hence suspected of possible mis-coding by Sri Lankan assistants The likelihood of a measles
outbreak emerging only in a single location without spreading to other areas, given that it is a viral disease, is highly unlikely.
The assistant entering the data had submitted data for “Toxide vaccine” as Tetanus.
These diseases occurred only once in one location
Uniformity of geographic distribution of disease cases ( low: concentrated in a few locations, high: spread over)
Fever greater than 7 days concentrated in February and March of 2010, mainly from a single location, during the non rainy seasonH
isto
rical a
ccu
mu
late
d c
ase c
ou
nts
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Data digitization: Some Feedback
“Integrated Disease Surveillance Program Data Entry Operator and Data Manager fear they will lose their jobs if mHealthSurvey and TCWI are introduced. At present these staff members receive phone calls from all health facilities and enter the data in spreadsheets of tabulation of weekly aggregates.” - Senior Project Officer, RTBI, India (19.08.10).
“Data digitizing nurses in India and assistants in Sri Lanka invest their own resources to repair and replace ill-fated mobile phones.” - Field Coordinator, Rural Technology and Business Incubator, India, consulted 18-December-2010 and Field Coordinator, Sarvodaya, Sri Lanka, consulted (26.04.10).
“In the present day setup in Sri Lanka, most of the surveillance data comes from Inward admissions and it is important that the data is collection is expanded to the Outpatient Departments as in the case of this project.” - Wayamba Provincial Director of Health Services, Sri Lanka, consulted (05.04.10).
“Sarvodaya Suwadana Center (primary health center) assistants in Sri Lanka have formed a social network to keep each other informed of escalating health situations in their communities” - Field Coordinator, Kurunegala District, Sri Lanka, consulted (06.10.10).
“For notifiable disease cases, digitizing the patient’s name and address is important for house investigations.” - Village Health Nurse (Keelsevalipatty), workshop report, Sivaganga District, India, consulted (01.10.10).
“It was easier for central officials in Chennai and New Delhi to monitor our individual statistics and performance opposed to scanning through paper or aggregated for the same; therefore, we are afraid to digitize data.” - Village Health Nurses (Nerkupai), Sivaganaga District, India, consulted (29.09.10).
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mHealth dala collection lessons
Nurses sending data
Near zero noise because impacts their work
No time to enter data patient care and routine work comes first
Under reporting to avoid extra work
Improvise mHealthSurvey for collection and reporting of other
Older slow to learn but will catchup
No prior experience beyond voice
Resolve technical problems on their own relative to PCs
Replaced handsets on their own
Outsourced data entry clerks
No incentive because 1) lack of knowledge 2) not direct impact
Data entry is their only job
No strings attached with reporting quantity
Nothing like that
Young were quick to learn
Knew all capabilities of mobile
Resolve technical problems on their own relative to PCs
Replaced handsets on their own
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Evaluation metric verticals and horizontals
Three verticals – data collection, event detection and reporting
Four layers – social, content, application, Transport
Arrows showing the Interoperability between layers and verticals
Objectively assess by calculating various indicators: costs, efficiencies, error rates, etc
Subjectively assess through interviews and simulations
Talk aboutthis
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T-Cube Web Interface (TCWI) by Auton Lab
AD Tree data structure
Trained Bayesian Networks
Fast response to queries
Statistical estimations techniques
Data visualization over temporal and spatial dimensions
Automated alerts
Slide 31 of 24 Copyright © 2009 by Artur Dubrawski
Bio-surveillance
Interactive analyticsAstrophysics
Food safety
Nuclear threat detection
Learning Locomotion
Safety of agriculture
Fleet prognostics
United Nations CTBTO
Saving sea turtles
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Pre-Screening using Massive Tempotal Scan
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T-Cube Web Interface – Spatio – Temporal Presentation
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Overview of the T-Cube data structure and computations
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Evaluation of TCWI
Replication study :: Sri Lankan Weekly Epidemiological Return (WER) reports published at www.epid.gov.lk notifiable disease counts tabulated by District was semi synthesized by distributing the weekly counts as daily counts taking day-of-week effect, gender distribution, and age representations.
Study the reliability and effectiveness :: significant events detected by T-Cube is compared with the ground truth and also weighed on the response actions or inaction
Competency exercise :: injected fake data over a period of 5 days and the subjects, unaware of the prefabricated events, were asked to detect most significant events
T-Cube Acceptance :: a questionnaire was designed based on the Technology Assessment Methodology (TAM) and was subject to TCWI users as well as health official associated with T-Cube who make decisions on whether or not to take action
Cost analysis :: compare the economic efficiencies and cost effectiveness between present detection/analyses system and T-Cube
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Replication study using synthesized WER data
We took 2007 – 2009 Weekly Epidemiological Returns publicly available data - http://www.epid.gov.lk/
Synthesized the data to match that similar to the RTBP dimensions by distributing the district weekly aggregates
- day-of-week visitation densities (M - F)
- female to male ration
- age-groups (0-5, 6-14, 15-20, 21-45, 46-65, above 65)
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Food poison spike as detected by spatial scan around Feb 15,2007 in Nuwara_Eliya, which was reported as outbreak by health department.
In addition TCWI detected spikes in Kandy and Vauvniya areas
Spatial scan is run by 7 days windows size.
Replication study using Sri Lanka WER data 2007 - 2009
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Another Food poison spike as detected by spatial scan around June 17,2009 in Nuwara_Eliya, the same location.
Spatial scan is run by 7 days windows size.
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Dengue Fever Seasonal and spatial pattern
May 1,2007
Aug 30,2007
May 21,2008 April 15,2009 May 28,2009
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First day an elevated global score noted, lead by region Kandy
4/14
Progression of Dengue Fever outbreak in April – June 2009
Spatial Scan global Score
4/15
Situation in Kandy intensified, together other regions
4/24
Southern Regions began to see increased cases
5/28
Southern region continue to see progression, while other region subsides
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A recent outbreak of Acute Diarrheal Disease in Thirukostiyur area
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Most frequently occurring wide spreading infectious disease outbreaks
Cough, Kurnegala District – Sri Lanka, 11 outbreak episodes to date with over 12,100 cases.
Respiratory Tract Infection, Kurnegala District – Sri Lanka, 09 outbreak episodes to date with over 18,547 cases.
Tonsilitis, Kurnegala District – Sri Lanka, 07 outbreak episodes to date with over 5.086 cases
Respiratory infectious diseases, a correlated with environmental factors, are the most common
These findings are from TCWI's spatial scan algorithms
Common cold is the most popular but gastrointestinal infectious are, relatively, the most visible
Common Cold, Sivaganga District – India, 18 outbreak episodes to date with over 23,188 cases.
Worm Infestation, Sivaganga District – India, 13 outbreak episodes to date with over 1,236 cases.
Dysentery, Sivaganga District – India, 5 outbreak episodes to date with over 1,541 cases.
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Trends in selected Chronic disease
Hypertension (High Blood Pressure) has a linearly increasing trend over the one year period in both countries with Females and Males over 45 years of age showing to be the most vulnerable. The dtrend in India shows an unusual increase between March and May 2010; while the reported cases are consistent throughout the year in Sri Lanka.
Diabetes-Mellitus has a linearly increasing trend over the one year period in both countries with Indians over 40 years of age and Sri Lankan over 45 years of age to be the most vulnerable groups.
Given that the Male to Female ratios, approximately, in Tamil Nadu, India and Kurunegala, Sri Lanka are both 1 : 1; statistics to date show females to be more susceptible to the above mentioned life style diseases.
These findings are from TCWI's statistical estimation and pivot table analysis methods
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Trends in selected Chronic disease
Arthritis and Rheumatoid-Arthritis has a linearly stagnate trend over the one year period in both countries with Males over 45 years of age and Females over 35 years of age to be the most susceptible in India; similarly Males over 45 and Females over 31 years of age to be the most vulnerable groups.
Asthma has a linearly decreasing trend over the one year period in both countries; the dtrend shows the counts to increase during the rainy season, India: Sept'09-Jan'10 and Sri Lanka: Nov '09-Jan '10. In India, only males over 45 years of age are affected but females in all age groups are affected. Both Male and Female over 31 years of age are in Sri Lanka are equally vulnerable.
Given that the Male to Female ratios, approximately, in Tamil Nadu, India and Kurunegala, Sri Lanka are both 1 : 1; statistics to date show females to be more susceptible to the above mentioned life style diseases.
These findings are from TCWI's statistical estimation and pivot table analysis methods
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TCWI Competency Assessments with Injected Synthetic data
Susceptible Exposed Infected Recovered
With a Network Flow
Injected 3 sets of data1) Notifiable disease :: Dysentery2) Other-Communicable disease :: ADD3) Syndrome :: Fever, Pain, RTI
Used “Epigrass” to generate synthetic data with a SEIR model
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TCWI Simulation results
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TCWI Actual Usage by Health Departments
Day-of-Week TCWI is Utilized Time spent each time
3 of 14 potential users spend less than 30 minutes each time once a week on detection analysis; remaining 9 did claim to be too busy to use TCWI
75% of the 9 Sri Lankan users spend more than 30 minutes each time every day of the week on detection analysis.
Day-of-Week TCWI is Utilized Time spent each time
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TCWI Preferred functions
India health officials' primary preferences are screening for fever, other-communicable diseases, and using the pivot table.
Sri Lanka health officials' primary preferences are screening the notifiable, fever, and other-communicable diseases.
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TCWI Technology Assessment Model scores
Technology Acceptance Model was applied to obtain these results on perceived ease of use, perceived usefulness, behavioral interaction, attitude towards using, and psychological attachment
SRI LANKN
The personal feeling is such that, all things considered, TCWI in the job is - quite a good idea, slightly beneficial, quite a wise idea, and slightly positive
INDIAN
This part of the questionnaire was not completed.
Users attitude towards using TCWI
The TAM questionnaire was conducted with 14 Indian and 09 Sri Lankan users (health officials and health workers)
STRONGLY AGREE
AGREE
IMPARTIAL
DISAGREE
STRONGLY DISAGREE
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T-Cube Web Interface: Some Feedback
“We can use this rich and comprehensive dataset and analysis tools for our annual planning,now our planning relies on professional perception and not necessary data.”
- Deputy Director Planning, Kurunegala District, Sri Lanka, Consulted (06.10.09)
“Epidemiologists want TCWI to facilitate the old ways of monitoring outbreaks based on thresholds opposed to statistical significance. For example, a single case of Malaria is regarded as anoutbreak in India, which requires response actions.”
- Deputy Director of Health Services, Sivaganga District, India, Consulted (19.12.09).
“It is important to monitor escalating fever cases, notifiable disease cases, and common clusters of symptoms.”
- Regional Epidemiologist, Kurunegala District, Sri Lanka, consulted (19.12.09).
“Medical Officers, Nurses, Health Educators, etc, who are interested in learning of outbreakssee the benefit and are happy with TCWI detection analysis methods but the staff at the Integrated Disease Surveillance Program are not ready to accept change and want to stick to thetraditional system unless state or national level Authorities mandate it.”
- Senior Project Officer, RTBI, India, consulted (19.08.10).
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T-Cube Web Interface: Some Feedback
“Pharmacists’ perceptions are such that a separate computer shouldbe given for detection analysis and they do not want to share their computers, which are usedfor medicine and birth information.”
- Senior Project Officer, RTBI, India, Consulted (08.07.2010).
“RTBP’s real-time biosurveillance capabilities will enhance the present day passive or non-active passive surveillance to an active surveillance system.”
- Wayamba Provincial Director of Health Services, consulted (07.07.10).
“All cases can be viewed in TCWI in real-time for detecting outbreaks swiftly, which other-wise would take several days before the hospitals/clinics send the notification paper forms, bywhich time the patient may be dead or discharged.”
- Public Health Inspector, Wariyapola, Sri Lanka, consulted (26.04.10).
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TCWI some early lessons
Health departments unfamiliar with statistical estimation methods for detection analysis
Requirements were to observe :: Integrated Disease Surveillance Program (IDSP) P/S disease list
No incentives to use and usage is almost nil
Health departments unfamiliar with statistical estimation methods for detection analysis
Requirements were to observe :: set of Notifiable disease and Weekly Epidemiological Returns report
Have incentives and using due to known delays in present system
Studying the TCWI Acceptance through TAM
RESULTS TO BE ANNOUNCED
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Evaluation metric verticals and horizontals
Three verticals – data collection, event detection and reporting
Four layers – social, content, application, Transport
Arrows showing the Interoperability between layers and verticals
Objectively assess by calculating various indicators: costs, efficiencies, error rates, etc
Subjectively assess through interviews and simulations
Talk aboutthis
www.lirneasia.net
Existing methods of receiving health alertsSurvey responses from 28 health workers from June 2009 to March 2010
At present health workers learn of adverse health events through MEDIA and WORD-OF-MOUTH, in some cases from PEERS
No formal Government method for sharing health risk information with health workers
Survey responses from 15 health workers from June 2009 to March 2010
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How do we integrate the subscribers and publishers?
How do we deliver early warnings in local language?
How do we use existing market available technologies?
How do we disseminate alerts over multiple channels?
How do we inter-operate between incompatible systems?
How do we effectively communicate the optimal content?
How do we address the communication strategy?
How do we accommodate upstream-downstream alerting?
Problem to solve
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Common Alerting Protocol Overview
□ All you want to know in “CAP Cookbook”
□ XML Schema and Document Object Model
□ Interoperable Emergency Communication Standard
□ Specifically geared for Communicating Complete Alerts
□ Capability for Digital encryption and signature X.509
□ Developed by OASIS for “all-hazards” communication
□ Adopted by ITU-T for Recommendations X.1303
□ Incubated by W3C Emergency Information Interoperability Framework
□ Used by USA, USGS, WMO, Gov of CA
□ Can be used as a guide for structuring alerts
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CAP Document Object Model
Bold elements are mandatory
Bold elements in <Alert> segment are qualifiers
Others elements are optional
Profile may specify other mandatory elements from optional list
Single <Alert> segment
Multiple <Info> segments inside <Alert> segment
Multiple <Area> and <Resource> segments inside a <info> segment
(*) indicates multiple instances are permitted
AlertidentifiersenderSentStatusmsgTypeSourceScopeRestrictionAddressCode (handling code)NoteReferences (Ref ID)Incidents (Incident ID)
InfoLanguageCategoryEvent*responseTypeUrgencySeverityCertaintyAudienceeventCode*Effective (datetime)Onset (datetime)Expires (datetime)senderNameHeadlineDescriptionInstructionWeb (InformationURL)Contact (contact details)Parameter*
ResourceresourceDescmimeTypeSizeURIderefURIdigest
AreaareaDescPolygon*Circle*Geocode*AltitudeCeiling
*
*
*
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Predefined values
CAP Element Predefined Values
<Status> Actual, Exercise, System, Test, Draft
<msgType> Alert, Update, Cancel, Ack, Error
<Scope> Public, Restricted, Private
<Language> en, fr, si, tm, …| codes ISO 639-1
<Category> Geo, Met, Safety, Security, Rescue, Fire, Health, Env, Transport, Infra, CNRNE, Other
<responseType> Shelter, Evacuate, Prepare, Execute, Monitor, Assess, None
<Urgency> Immediate, Expected, Future, Past, unknown
<Severity> Extreme, Sever, Moderate, Minor, Unknown
<Certainty> Observed, Likely, Possible, Unlikely, Unknown
<Area> b-WGS 84
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Prioritizing Messages in CAP
Priority <urgency> <severity> <certainty>
Urgent Immediate Extreme Observed
High Expected Severe Observed
Medium Expected Moderate Observed
Low Expected Unknown Likely
Select value
Auto populate
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Sahana Alerting Broker (SABRO) Subsytems
❏ Inputs can be manual or automated
❏Message creation & validation uses CAP v1.1 and EDXL 1.0 data standards
❏Access control (permissions) and user rules are governed through the Organization Resource Manager (ORM)
❏Direct alerts are sent to end user recipients and Cascade alerts are a system-to-system communication determined by the message distribution method
❏Long-text, Short-text, and Voice-text are different forms of full CAP message for the ease of message delivery to various end-user terminal devices
❏Message acknowledgement logs the recipient messages confirming receipt
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Overview of Sahana
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Sahana Messaging/Alerting CAP/EDXL Broker by Respere
Single input multiple output engine; channeled through multiple technologies
Manage publisher /subscribers and SOP
Adopt PHIN Communication and Alerting Guidelines for EDXL/CAP
Relating the template editor with the SMS/Email Messaging module
Do direct and cascading alert from a regional jurisdictional prospective
Designing short, long, and voice text messages
Addressing in multi languages
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Example of template for SMS alert
<headline> : <status><msgType> for <areaDesc> area with
<priority> priority <event> issued by <senderName>.
Msg: <identifier> sent on <sent>Desc: <description> More detailsWeb: <web>Call: <contact>
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Examaple of Automated Standard Message
Escalating mumps in Kurunegala district for Wariyapola-PHI area
A low priority notifiable disease outbreak issued by Dr Hemachandra.
Msg : nwpdhs-1281246871 sent on 2010-08-08 11:08:57.
Desc : 2 cases of Mumps for 15-20 age group and all genders were reported in Munamaldeniya.
More DetailsWeb www.scdmc.lkCall 2395521
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CAP XML → XSL → delivery method
Single Input Multiple Output Mass Messaging; towards a publisher subscriber model
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CAP short/long text Message delivery methods
Single Input Multiple Output Mass Messaging; towards a publisher subscriber model
Community Suwadana Health Centers
Government Regional Epidemiology and Medical Officer of Health
departments
Government Regional Epidemiology and Medical Officer of Health
departments
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Steps for setting up a CAP Profile
Audience <Scope>Alert First Responders only (i.e. closed user group)
Example: police, health workers, civil society, public servants
Alert Public (entire population)
Combination of First Responders and Public
step 1: alert First-Responders to give them time to prepare
Step 2: warn public
Geographical Descriptions <Area>Country wide
Province or State
District
Other – Geocodes or GPS polygons
National <Languages>English only or Chinese only or Malay onlyEnglish, Hindi, Chinese, and Malay
Communications Technology?Mobile phones – SMS, Cell Broadcast, Email, AppletTV – Text, Audio, VisualAM/FM Radio - Text, AudioVHF/UHF Radio - AudioInternet – HTTP, Email, Webservices
Audience
Geography
Language
Technology
- determining the policy and procedures -
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Downstream messaging structure - INDIA
IDSP PHC
Message Creator – IDSP staff member Message Creator – PHC staff member
Message Issuer - DE Message Issuer - MO
Action alert
Mode of delivery *1 SMS2 Short Email
3 Long Email
Awareness message
Awareness Message
Mode of delivery*1 SMS2 Short Email3 Long Email
Action Alert
RecipientsBMOMOHISHNVHN
RecipientsBMOMOHISHNVHNOther health officials at IDSP
RecipientsBMOMOHI
SHNVHN
Other health officials at IDSP
RecipientsMOSHNHI
VHN
Event Detection
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Downstream messaging structure – SRI LANKA
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Messaging exercises with Sahana Alerting Broker3 users in India and 5 users in Sri Lanka participated in the message dissemination exercises. Each user was presented with four varying scenarios in relation to escalating cases of diseases identified through TCWI and other sources.
Percentage of messages sent on-time (benchmark time-to-completion was 5 minutes)
The security policy of the software, by default, is set to expire the session after 5 minutes to prevent unauthorized use, which forced the user to restart.
Accuracy of creating the messages with populating the common alerting protocol attributes of the software
Templates with pre-populated values and a clear structure helped the users with creating the messages
Correctly selecting the appropriate delivery channels targeting the intended recipients
It was easier to comprehend issuing of alerts but not the the same with issuing situational awareness messages such as the weekly top 5 diseases reports.
INDIA Exercises were incomplete; no results to discuss
35%
65%
30%10%
10%
50%
35%
10% 55%
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3 2 1 0Error0
50
100
Msg received via?
Affected locations?
Event?
Who issued?
Msg Identifier?
Msg priority?
Response actions?
Get more info?
Points
No
of s
ub
ject
s ga
ve a
nsw
er
Que
stio
ns
3 2 1 0Error0
50
100
Msg received via?
Affected locations?
Event?
Who issued?
Msg Identifier?
Msg priority?
Response actions?
Get more info?
Points
No
of s
ub
ject
s ga
ve a
nsw
er
Que
stio
ns
CAP SMS Alert/Situ-aware comprehension exercises
Outcomes
Everyone did quite well in the exercises except for 1 or 2 exceptional cases
Both India and Sri Lanka having trouble with msg-identifier; could be because msg-identifier getting truncated by the 160 char SMS constraint
Recommendation :: put msg- identifier in subject header (but may cutoff rest due to 160 char SMS); use the term “reference number” instead or both
Assessment design
Participants receive 4 SMS text with varying values of the CAP attributes
India = 23 and Sri Lanka = 19 health workers participated in the exercise
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Credibility, Persuasiveness, Validity
Counts, disease, locations, response
CAP Short-text message over SMS, 84 responses for 4 different messages
CAP Short-text message over SMS, 76 responses for 4 different messages
32%
14%8%
28%
18%
Message Authenticity
Call MOHCall IssuerRefer In-ternet
OtherNo Ans
45%
8% 14%
18%
14%
Verify Authenticity?
Call MOHCall IssuerRefer In-ternet
OtherNo Ans
72%
11%
7%11%
Summarize message:
Disease locations sender
Disease locations sender re-sponse
Other
No Ans
17%
7%
7%
70%
Recommendations:
Mention patient de-tails
AdequateOther
No Ans
25%
3%
14%9%
49%
Other Delivery
EmailEmail & Web
Voice
OtherNo Ans
Email, Web, VoiceSender NamelocalizeUse Web/Contact
Expected response
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Some assumptions
❒ The health facilities serve, on average, 100 patients a day; hence, the digitizing capacity of the a single data-entry assistant or single nurse is constrained by this number
❒ Calculations for the existing programs are based on least case and the costs for the RTBP are based on the worst case; e.g. accounted for annual training in RTBP but not in existing system
❒ Indian Primary Health Center (PHC) is both a health facility and a health department; Sri Lanka Hospitals/Clinics and MOH are separate
❒ Reporting is confined to PS List and Morbidity (India); H544, H399, H499, WER (Sri Lanka)
❒ No software licensing fees included such as for the T-Cube Web Interface but that is less than 5% of TCO
❒ Considered only total cost of ownership for the economic analysis, did not incorporate impact related costs such as quality adjusted life years, productivity improved, lives saved, etc
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TCO macro-costs and the marginal differences
Explanation of marginal difference of RTBP macro cost > 20% than existing system
System delivery :: unable to get actual program design, development, and implementation cost, most likely funded by INGO, however, the per district monthly cost is very small.
System Admin/Support :: no established budget, each department spends money for repairs as and when needed. RTBP accounts for it.
Data Center :: India – DPH&PM system is one component of several managed by the National Information Center, in comparison to decentralizing the data centers to be managed at districts
Health Facilities :: major portion of the cost is the new human resource bundled with technology for health record digitization
System delivery, system support, and data center costs are < 7% of overall cost; hence the focus of the economic analysis is on the bulk: health facilities, health departments, and health workers
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Total Cost of Ownership
India and Sri Lanka invest very little or no resources on real-time event detection and alerting, ~ 88% in data collections
RTBP can reduce TCO > 35%, moreover, increase timeliness, and introduce rapid detection and alerting
Existing trend analysis is for long term planning only; dual data-entry at departments.
[ Existing (IN) = present system in India (Integrated Disease Surveillance Program); Existing (LK) = present system in Sri Lanka (Disease Surveillance and Notification Program); RTBP (IN), RTBP (LK) = Real-Time Biosurveillance Program in India and Sri Lanka, respectively]
Comparison of expenses in relation to the data collection, event detection, and alerting components
Digitizing data at the point of care removes the bulk health department expenses of labor intensive data aggregation and consolidation.
Worst case scenario of bundling frontline data digitizing with new resource person increases the health facility investment.
Health facility cost increase < health department money saved; India: 61% < 86%, Sri Lanka: 72% < 87%
Comparison of expenses in relation to the health facility, health department, and health workers
Comparison of Entity Costs in India and Sri Lanka- existing paper-based vs introduced RTBP
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Incremental Cost Effectiveness Ratios (ICER)
Existing ExistingRTBP RTBP
Going from Existing system to RTBP
Data collection – cheaper, more data, more attributes, data available same day (No further analysis required)
Event Detection – cheaper in Sri Lanka, almost same in India, real-time/automated event detection, reports at the touch of a button, globally accessible, no humans needed to feed data, better visualization and analystcs (no further analysis required)
Alerting – new investment and new concept but health workers will be better informed of the regional health status for preparedness and early response (needs further analysis)
Relative RTBP Cost
Relative RTBP
benefits
Expensive & ineffective→
resource unacceptable
Existing
Expensive & effective→
needs further analysis
Cheaper & ineffective→ needs further
analysis
Cheaper & effective→ no
further analysis
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ICT Sy
stem de
livery
Syste
m Adm
in/S
uppo
rt
Data ce
nter
Health
facil
ity
Health
depart
ment
Health
wor
ker
0.00
5,000.00
10,000.00
Alerting (USD)Detection (USD)Collection (USD)
Internal EntitiesCos
t (U
SD
) / D
istr
ict
/ Mon
th
NIC ID
SP Syste
m deliv
ery
Syste
m Adm
in/S
uppo
rt
Data ce
nter
Health
facil
ity
Health
depa
rtmen
t
Health
wor
ker0.00
10,000.00
20,000.00
Alerting / situ-awareEvent DetectionDate Collection
Internal EntitiesCos
t (U
SD
) / D
istr
ict
/ Mon
th
ICD/W
ER Syste
m deliv
ery
Syste
m Admin
/Sup
port
Data ce
nter
Health
facil
ity
Health
depa
rtmen
t
Health
work
er0.00
10,000.00
20,000.00
Alerting / Situ-awareEvent DetectionData Collection
Internal EntitiesCos
t (U
SD
) / D
istr
ict
/ Mon
thRTBP can fix the imbalance
Ideally health facilities should be powered for data collection, health departments for detection and alerting, with health workers fully on response
RTBP
Sri Lanka – health departments consume bulk of the resources
India/Sri Lanka – almost zero resources on detection and mitigation
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Economic Efficiencies :: India (annual cost per district)
Present IDSP RTBP
Report LIMITED morbidity/PS-list disease: 77
US$ 3,650.00 / disease
Report LIMITED morbidity/PS-List avg cases::600
470.00 / case
Program for District pop: 1150753US$ 0.24 / inhabitant
Report ALL diseases: 117US$ 800.00 / disease
Report ALL cases: 6175 avg per month15.00 / case
Program for District pop:1150753US$ 0.08 / inhabitant
Data Collection
Event Detection
Monitor LIMITED PS-list disease: 25US$ 402.00 / disease
Program for District pop: 1150753US$ 0.01 / inhabitant
Monitor ALL disease: 117US$ 135.00 / disease
Program for District pop: 1150753US$ 0.01 / inhabitant
Alerting
Disseminate LIMITED PS List disease: 25650.00 / disease
Program for District pop: 1150753US$ 0.01 / inhabitant
Disseminate Communicable disease: 43US$ 800.00 / disease
Program for District pop: 1150753US$ 0.03 / inhabitant
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Economic Efficiencies :: Sri Lanka (annual cost per district)
Present SNS RTBP
Report LIMITED Notifiable disease: 25US$ 10,800.00 / disease
Report LIMITED WER cases::70321.00 / case
Program for District pop: 1550000US$ 0.17 / inhabitant
Report ALL diseases: 179US$ 570.00 / disease
Report ALL cases: 22835 avg per month4.50 / case
Program for District pop:1550000US$ 0.07 / inhabitant
Data Collection
Event Detection
Monitor LIMITED Notifiable disease: 25US$ 311.00 / disease
Program for District pop: 1550000US$ 0.01 / inhabitant
Monitor ALL disease: 179US$ 54.00 / disease
Program for District pop: 15500US$ 0.01 / inhabitant
Alerting
Disseminate LIMITED PS List disease: 25285.00 / disease
Program for District pop: 1150753US$ 0.01 / inhabitant
Disseminate Communicable disease: 49US$ 630.00 / disease
Program for District pop: 1150753US$ 0.03 / inhabitant
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One-way Sensitivity Analysis
Parameter: Typical Value India Sri Lanka
(I) Health Districts per State 32 2(II) Health Facilities per District 47 44(III) Health Departments per District 48 21(IV) Health Workers per Facility 10 21(V) Currency exchange rate to US$ 45.00 110.00
Mobilizing a Health Worker in the existing system with the infrastructure: paper forms,books, archiving cupboards, workspace and operational expenses: travel costs is much greater than using RTBP technology: mobile phones and simple server with affordable network infrastructure
RTBP technology reduces labor, duplicate data entry, consolidation/aggregation, and manual analyses required by health departments
Digitizing of all patient records in RTBP system may require introducing a new staff member at the health facility
Sensitivity was applied to only parameters II – IV that affect individual components differently. Parameters I and V are direct proportionality that affects the entire cost and not individual components.
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Conclusions mhealthSurvey is a worthy candidate for patient disease/syndrome digitization;
however, must be robust to minimize the noise and delays; need a better GUI if Medical Officers are to enter high volume real-time data opposed to a data entry clerk
Need a complete and comprehensive standardized disease syndrome ONTOLOGY perhaps a hybrid of SNOMED-CT and LOINC
T-Cube false alarm rates must be minimized through the iterative enhancement and machine learning
Sahana CAP Broker SMS, Email, and Web messaging has proven to be a winner for real-time adverse health event information dissemination but need Voice as well
Although the value is seen in T-Cube Web Interface and CAP/EDXL Messaging The policies must be reformed to go beyond the century old paper based system to using ICT based event detection and alerting/situational-awareness
There should not be any institutional fears arising from the cost reductions instigated by the introduction of ICTs (e.g. RTBP) as is will still require the same work force
Before the cost benefits can take affect the laws and regulations must be changed to remove the paper and the storage cupboards that are government mandates
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Conclusions
RTBP costs are less, benefits are better, and efficiency gains are higher than the existing disease surveillance and notification systems
The laws and regulations must be changed to replace the legal forms and registers with electronic health records.
Health record security, privacy, and unique identifiers must be addressed before national implementation
Be ready to accept change; especially the paradigm of comprehensive disease/syndromic active surveillance without paper
There is a severe cost associated with false alarms (false positives) and missed alarms (true negatives), which needs further study and rectifying
Project Partners:
International Development Research Center, Canada - www.idrc.ca
Lanka Jathika Sarovodaya Shramadana Society, Sri Lanka - www.sarvodaya.org
IIT-Madras's Rural Technology and Business Incubator, India - www.rtbi.in
Carnegie Mellon University Auton Lab, USA - www.autonlab.org
University of Alberta, Canada - www.ualberta.ca
National Center for Biological Sciences, India - www.ncbs.in
Respere Lanka (Pvt) Limited, Sri Lanka - www.respere.com
LIRNEasia, Sri Lanka - www.lirneasia.net
Ministry of Health and Nutrition, Wayamba Privince, Sri Lanka - http://www.health.gov.lk/
Health and Family Welfare Department, Tamil Nadu, India - http://www.tnhealth.org/dphpm.htm
Listed in alphabetical order
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References related to RTBP[1] S. Prashant and N. Waidyanatha (2010). User requirements towards a biosurveillance program, Kass-Hout, T. & Zhang, X. (Eds.). Biosurveillance: Methods and Case Studies. Boca Raton, FL: Taylor & Francis, Chapter 13, pp .240-263.[2] Kannan T., Sheebha R., Vincy A., and Nuwan Waidyanatha (2010). Robustness of the
mHealthSurvey midlet for Real-Time Biosurveillance, Proceedings of the 4th IEEE International
Symposium on Medical Informatics and Communication Technology (ISMICT '10), Taipei, Taiwan.[3] M. Sabhnani, A. Moore, and A. Dubrawski (2007). Rapid processing of ad-hoc queries against large sets of time series. Advances in Disease Surveillance, Advances in Disease Surveillance, Vol 2, 2007.[4] S. Ray, A. Michalska, M. Sabhnani, A. Dubrawski, M. Baysek, L. Chen, J, and Ostlund (2008). T-Cube Web Interface: A Tool for Immediate Visualization, Interactive Manipulation and Analysis of Large Sets of Multivariate Time Series, AMIA Annual Symposium, 2008:1106, Washington, DC, 2008.[5] A. Dubrawski, M. Sabhnani, S. Ray, J. Roure, and M. Baysek (2007). T-Cube as an Enabling Technology in Surveillance Applications. Advances in Disease Surveillance 4:6, 2007.[6] G. Gow and N. Waidyanatha (2010). Using Common Alerting Protocol To Support A Real-Time Biosurveillance Program In Sri Lanka And India, Kass-Hout, T. & Zhang, X. (Eds.). Biosurveillance: Methods and Case Studies. Boca Raton, FL: Taylor & Francis, Chapter 14, pp 268-288.[7] G. Gow, Vincy. P.., and N. Waidyanatha (2010). Using mobile phones in a Real-Time Biosurveillance Program: Lessons from the frontlines in Sri Lanka and India. Proceedings of the 2010 IEEE International Symposium on Technology and Society (ISTAS '10), New South Wales, Australia.[8] Ganesan M., S. Prashant, Janakiraman N., and N. Waidayanatha (2010), Real-time Biosurviellance Program: Field Experiences from Tamil Nadu, India. IASSH conference paper. Varanasi, Uttarpradesh, India.[9] A. Dubrawski (2009). Detection of Events in Multiple Streams of Surveillance Data. In Infectious Disease Informatics: Public Health and Biodefense, Eds. C. Castillo-Chavez, H. Chen, W. Lober, M. Thurmond, and D. Zeng. Springer-Verlag (in press).[10] D. Neill, and G. Cooper (2009). A Multivariate Bayesian Scan Statistic for Early Event Detection and Characterization. Machine Learning (in press).[11] M. Wagner (2008). Methods for testing Biosurveillance systems, Handbook of Biosurveillance (eds. Wagner, M., Moore, M., and Aryel, R.), pp 507-515, Elsevier academic press.
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